Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the NASA/GMAO Seasonal Forecast System

We assess the impact of satellite sea surface salinity (SSS) observations on dynamical ENSO forecasts. Assimilation of SSS improves the mixed layer depth (MLD) and modulates the Kelvin waves associated with ENSO. In column 2, the initialization differences between experiments that assimilate SSS minus those withholding SSS assimilation are presented. Column 3 shows examples of forecasts generated for the different phases of ENSO assimilating the different satellite SSS. In general, for all phases of ENSO, SSS assimilation improves forecasts. The far right column compares ensemble means for assimilation of individual and combined SMOS, Aquarius, and SMAP SSS forecasts. Finally, the latest forecasts are presented comparing assimilation versus no- assimilation of satellite SSS for single forecasts over the last year

Should you test or treat pregnant women with a history of pregnancy-related VTE?

No high-quality evidence supports testing for thrombophilia in pregnant patients who have experienced venous thromboembolism (VTE) in a previous pregnancy. Antepartum and postpartum anticoagulation with LMWH produces lower rates of VTE in patients with a prior history of VTE in pregnancy. Pregnant women with a prior history of VTE who are not treated with anticoagulation have about a 5% risk of antepartum or postpartum VTE. Expert opinion recommends graduated compression stockings

What are the causes of elevated TSH in a newborn?

Congenital hypothyroidism is a critical cause of elevated thyroid-stimulating hormone (TSH) in newborns; evaluate all neonates with an elevated TSH for congenital hypothyroidism (strength of recommendation [SOR]: A). Other causes of an elevated TSH include transient hypothyroidism due to neonatal illness, prematurity, iodine excess or deficiency, and maternal medication or maternal thyroid disease

Impact of Aquarius and SMAP Sea Surface Salinity Observations on Seasonal Predictions of the 2015 El Nino

We assess the impact of satellite sea surface salinity (SSS) observations on dynamical ENSO forecasts for the big 2015 El Nino event. From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP (Soil Moisture Active Passive Mission), allows a unique opportunity to compare and contrast forecasts generated with the benefit of these two satellite SSS observation types. Four distinct experiments are presented that include 1) freely evolving model SSS (i.e. no satellite SSS), relaxation to 2) climatological SSS (i.e. WOA13 SSS), 3) Aquarius, and 4) SMAP initialization. Coupled hindcasts are then generated from these initial conditions for March 2015. These forecasts are then validated against observations and evaluated with respect to the observed El Nino development

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GMAO S2S Forecast System

El Nino/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would be of great benefit for society. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean density. Satellite Sea Surface Salinity (SSS), combined with temperature, help to identify ocean density changes and associated mixing near the ocean surface. We assess the impact of satellite SSS observations for improving near-surface dynamics within ocean analyses and how these impact dynamical ENSO forecasts using the NASA GMAO (Global Modeling and Assimilation Office) Sub-seasonal to Seasonal (S2S_v2.1) coupled forecast system (Molod et al. 2018 - i.e. NASA's contribution to the NMME (National Multi-Model Ensemble) project). For all initialization experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF ( Local Ensemble Transform Kalman Filter) scheme similar to Penny et al., 2013. A separate reanalysis additionally assimilates Aquarius V5 (September 2011 to June 2015) and SMAP (Soil Moisture Active Passive) V4 (March 2015 to present) along-track data.We highlight the impact of satellite SSS on ocean reanalyses by comparing validation statistics of experiments that assimilate SSS versus our current prediction system that withholds SSS. We find that near-surface validation versus observed statistics for salinity are slightly degraded when assimilating SSS. This is an expected result due to known biases between SSS (measured by the satellite at approximately 1 centimeter) and in situ measurements (typically measured by Argo floats at 3 meters). On the other hand, a very encouraging result is that both temperature, absolute dynamic topography, and mixed layer statistics are improved with SSS assimilation. Previous work has shown that correcting near-surface density structure via gridded SSS assimilation can improve coupled forecasts. Here we present results of coupled forecasts that are initialized from the GMAO S2S reanalyses that assimilates/withholds along-track (L2) SSS. In particular, we contrast forecasts of the overestimated 2014 El Nino, the big 2015 El Nino, and the minor 2016 La Nina. For each of these ENSO scenarios, assimilation of satellite SSS improves the forecast validation. Improved SSS and density upgrades the mixed layer depth leading to more accurate coupled air/sea interaction

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GEOS GMAO S2S Forecast System

We assess the impact of satellite sea surface salinity (SSS) observations on dynamical ENSO forecasts. Assimilation of SSS improves the mixed layer depth (MLD) and modulates the Kelvin waves associated with ENSO. In column 2, the initialization differences between experiments that assimilate SSS minus those withholding SSS assimilation are presented. Column 3 shows examples of forecasts generated for the different phases of ENSO. From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP, allows a unique opportunity to compare and contrast coupled forecasts generated with the benefit of these two satellite SSS observation types. The far right column compares assimilation of Aquarius, SMAP and combined Aquaries and SMAP on forecasts for the 2015 El Nino

EARLY NEOPROTEROZOIC CRUST FORMATON IN THE DZABKHAN MICROCONTINENT, CENTRAL ASIAN OROGENIC BELT

The Dzabkhan microcontinent was defined by [Mossakovsky et al., 1994] as a cratonic terrane with an early Precambrian basement that combines highgrade metamorphic complexes of the Songino, Dzabkhan, Otgon, Baidarik, Ider and Jargalant Blocks. However, early Precambrian ages have so far only been recognized in the Baidarik and Ider blocks [Kozakov et al., 2007, 2011; Kröner et al., 2015].The Dzabkhan microcontinent was defined by [Mossakovsky et al., 1994] as a cratonic terrane with an early Precambrian basement that combines highgrade metamorphic complexes of the Songino, Dzabkhan, Otgon, Baidarik, Ider and Jargalant Blocks. However, early Precambrian ages have so far only been recognized in the Baidarik and Ider blocks [Kozakov et al., 2007, 2011; Kröner et al., 2015]

MAIN STAGES OF CONTINENTAL CRUST FORMATION IN THE WESTERN ALDAN SHIELD: CONSTRAINTS FROM SM-ND ISOTOPE SYSTEMATICS OF CENOZOIC SANDS IN THE CHARA AND TOKKA BASINS

Previous geochronological and Sm-Nd isotopegeochemical studies have identified the main stages of the Precambrian continental crust formation in the central and eastern parts of the Aldan Shield [Kotov et al., 2006], while its western part (Chara-Olekma Geoblock) has not been adequately investigated yet in this respect.Previous geochronological and Sm-Nd isotopegeochemical studies have identified the main stages of the Precambrian continental crust formation in the central and eastern parts of the Aldan Shield [Kotov et al., 2006], while its western part (Chara-Olekma Geoblock) has not been adequately investigated yet in this respect

A multiscale analysis of gene flow for the New England cottontail, an imperiled habitat specialist in a fragmented landscape

Landscape features of anthropogenic or natural origin can influence organisms\u27 dispersal patterns and the connectivity of populations. Understanding these relationships is of broad interest in ecology and evolutionary biology and provides key insights for habitat conservation planning at the landscape scale. This knowledge is germane to restoration efforts for the New England cottontail (Sylvilagus transitionalis), an early successional habitat specialist of conservation concern. We evaluated local population structure and measures of genetic diversity of a geographically isolated population of cottontails in the northeastern United States. We also conducted a multiscale landscape genetic analysis, in which we assessed genetic discontinuities relative to the landscape and developed several resistance models to test hypotheses about landscape features that promote or inhibit cottontail dispersal within and across the local populations. Bayesian clustering identified four genetically distinct populations, with very little migration among them, and additional substructure within one of those populations. These populations had private alleles, low genetic diversity, critically low effective population sizes (3.2-36.7), and evidence of recent genetic bottlenecks. Major highways and a river were found to limit cottontail dispersal and to separate populations. The habitat along roadsides, railroad beds, and utility corridors, on the other hand, was found to facilitate cottontail movement among patches. The relative importance of dispersal barriers and facilitators on gene flow varied among populations in relation to landscape composition, demonstrating the complexity and context dependency of factors influencing gene flow and highlighting the importance of replication and scale in landscape genetic studies. Our findings provide information for the design of restoration landscapes for the New England cottontail and also highlight the dual influence of roads, as both barriers and facilitators of dispersal for an early successional habitat specialist in a fragmented landscape